Feature Engineering for Machine Learning : Principles and Techniques for Data Scientists - Alice Zheng

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Feature Engineering for Machine Learning

Principles and Techniques for Data Scientists

By: Alice Zheng

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Paperback | 30 April 2018

At a Glance

Paperback
630 Pages

Dimensions(cm)
24.5 x 15 x 2

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Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.

Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples.

You’ll examine:

Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms
Natural text techniques: bag-of-words, n-grams, and phrase detection
Frequency-based filtering and feature scaling for eliminating uninformative features
Encoding techniques of categorical variables, including feature hashing and bin-counting
Model-based feature engineering with principal component analysis
The concept of model stacking, using k-means as a featurization technique
Image feature extraction with manual and deep-learning techniques

About the Author

Alice is a technical leader in the field of Machine Learning. Her experience spans algorithm and platform development and applications. Currently, she is a Senior Manager in Amazon's Ad Platform. Previous roles include Director of Data Science at GraphLab/Dato/Turi, machine learning researcher at Microsoft Research, Redmond, and postdoctoral fellow at Carnegie Mellon University. She received a Ph.D. in Electrical Engineering and Computer science, and B.A. degrees in Computer Science in Mathematics, all from U.C. Berkeley.

Principal Product Manager + Data Scientist for Concur Labs at SAP Concur, designing prototypes, interfaces and future tech for travel and expense. Amanda experiments with projects and programs to make machine learning more accessible. Her side projects include volunteering with the NASA Datanauts and getting outside as much as possible.

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